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Last Updated: Mar 27, 2024

Named Entity Recognition

Leveraging ChatGPT - GenAI as a Microsoft Data Expert
Speaker
Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM

Introduction

Named Entity Recognition

Named Entity Recognition is an essential concept in natural language processing. If we want to develop any Natural Language Processing application, we cannot produce it without named entity recognition. The basic definition of named entity recognition detects named entities like Person, organization, place date, etc. For example, in the line- John lives in America and has worked for Microsoft since April 2020; the named entities are

1.John is a person

2.America is a place

3.April 2020 is a date

4.Microsoft is an organisation

You should have a question: why should we detect the named entities for natural language processing? In natural language processing, the first step is Tokenization. We need to tokenize a sentence into additional granule tokens, and then we need to determine the entity types for these tokens and samples. Named entity recognition as supervised machine learning, we need to teach the model based on the training data and labels. We use validation data for tuning the hyperparameters. When the model is trained,  we feed the unknown test data and check the accuracy metric of our model. 

When it comes to humans,  the category definition is intuitively clear. However, the classification can be a bit ambiguous when it comes to computers. 

Methods of Named Entity Recognition

  • We can use different machine learning algorithms to train the model for multiclass classification is one of the ways,  but it requires a lot of labelling. The model also requires a deep understanding of the context to deal with ambiguity in sentences. So, the task is quite challenging for simple machine learning.
  • NLTK and NLP's Speech tiger implement conditional random fields that allow terms to be modeled probabilistically. CRF Captures the context of the message very well. 
  • Deep learning uses a technique known as word embedding that can understand the syntactic relationship between words. The Deep Learning NER  is much more accurate than previous methods. The reason for this is that deep learning NER  assembles words much more accurately than the previous methods. 
  • In addition, Deep learning NERs Can also be trained to analyze high-level terminology and topic-specific terms. This makes them suitable for use in several situations.
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Applications of Named Entity Recognition

1. Human Resources

Improve internal workflow by categorizing employee complaints and questions and speeding up the hiring process by summarising applicants' resumes.

2. Customer Support

Respond to user request complaints and queries faster by categorizing them and filtering them by priority keywords.

3. Recommendation Systems

By summarising descriptive text, reviews, and discussions,  we can speed up and improve the relevance of search results.

4. Content Classification 

By identifying the topics and themes of blog posts and news articles,  we can surface content more easily and gain insight into trends.

5. Health Care

Extraction of essential information from laboratory reports improves Patient Safety and reduces workloads.

6. Academics 

Identify key terms, topics, and themes in papers and archives that will assist students and researchers in finding relevant material faster.

Frequently Asked Questions

1. Define Named Entity Recognition. 

Named entity recognition involves identifying a text to gather critical information and putting it into predefined categories.

 2. What are the processes involved in the Named Entity Recognition?

 There are two processes.

  •  Detect named entity
  •  Categorize The entity

3. What are the common entity categories?

The standard and city categories are Person, organization,  time,  location,  work of art, etc.

4. Name some open-source Named Entity Recognition APIs.

  • Standard named entity recognition (NER)
  • SpaCy
  • Natural language Toolkit (NLTK)

5. What is SpaCy?

SpaCy is an open-source library for Natural Language Processing written in Python and Cython. In addition to learning natural language recognition,  it can also be used to preprocess text for deep learning. 

Conclusion

To conclude this article, we have learned about NER and its various applications that can be used to detect named entities in texts.

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Further Readings-

Hidden Markov Model

HyperParameter Tuning and Predicting Scores

Topics covered
1.
Introduction
2.
Methods of Named Entity Recognition
3.
Applications of Named Entity Recognition
4.
Frequently Asked Questions
5.
Conclusion